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Cao YM, Zhang Y, Wang Q, Zhao R, Hou M, Yu ST, Wang KK, Chen YJ, Sun XQ, Liu S, Li JT. Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish. Curr Res Food Sci 2024; 9:100929. [PMID: 39628599 PMCID: PMC11612356 DOI: 10.1016/j.crfs.2024.100929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Revised: 11/14/2024] [Accepted: 11/16/2024] [Indexed: 12/06/2024] Open
Abstract
The polyunsaturated fatty acids (PUFAs), particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are critical determinants of the nutritional quality of fish. To rapidly and non-destructively determine the muscular PUFAs in living fish, an accuracy technique is urgently needed. In this study, we combined skin hyperspectral imaging (HSI) and machine learning (ML) methods to assess the muscular PUFAs contents of common carp. Hyperspectral images of the live fish skin were acquired in the 400-1000 nm spectral range. The spectral data were preprocessed using Savitzky-Golay (SG), multivariate scattering correction (MSC), and standard normal variable (SNV) methods, respectively. The competitive adaptive reweighted sampling (CARS) method was applied to extract the optimal wavelengths. With the skin spectra of fish, five ML methods, including the extreme learning machine (ELM), random forest (RF), radial basis function (RBF), back propagation (BP), and least squares support vector machine (LS-SVM) methods, were used to predict the PUFAs and EPA + DHA contents. With the spectral data processed with the SG, the RBF model achieved outstanding performance in predicting the EPA + DHA and PUFAs contents, yielding coefficients of determination (R2 P) of 0.9914 and 0.9914, root mean square error (RMSE) of 0.3352 and 0.3346, and mean absolute error (MAE) of 0.2659 and 0.2660, respectively. Finally, the visualization distribution maps under the optimal model would facilitate the direct determination of the fillet PUFAs and EPA + DHA contents. The combination of skin HSI and the optimal ML method would be promising to rapidly select living fish having high muscular PUFAs contents.
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Affiliation(s)
- Yi-Ming Cao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Yan Zhang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Qi Wang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Ran Zhao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Mingxi Hou
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Shuang-Ting Yu
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
- Chinese Academy of Agricultural Sciences, Beijing, 100141, China
| | - Kai-Kuo Wang
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China
| | - Ying-Jie Chen
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China
| | - Xiao-Qing Sun
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Shijing Liu
- Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200092, China
| | - Jiong-Tang Li
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
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Gao T, Saito Y, Miwa Y, Kuramoto M, Konagaya K, Yamamoto A, Hashiguchi S, Suzuki T, Kondo N. Non-destructive estimation of flesh oil content in avocado (Persea americana Mill.) using fluorescence images from 365-nm UV light excitation. Photochem Photobiol Sci 2024; 23:1871-1882. [PMID: 39287918 DOI: 10.1007/s43630-024-00636-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Accepted: 09/09/2024] [Indexed: 09/19/2024]
Abstract
The flesh oil content (OC) is a crucial commercial indicator of avocado maturity and directly correlates with its nutritional quality. To meet export standards and optimize edible characteristics, avocados must be harvested at the appropriate stage of physiological maturity. The significant variability in OC during maturation, without any external morphological indicators, poses a longstanding challenge. Currently, harvesting maturity is optimized through time-consuming, destructive laboratory methods like freeze-drying and chemical extraction, which use representative samples to estimate the maturity of entire orchards. In this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar. We developed a surface fluorescence index that strongly correlates with OC, achieving correlation coefficients up to - 0.91. Our non-destructive and rapid approach achieved a cross-validation accuracy with an R2 value of 0.81, enabling the classification of avocados with low and high OC. This pioneering method shows considerable potential for further improvement and refinement. This study lays the groundwork for developing a portable, cost-effective, and real-time method for non-destructive in situ monitoring of avocado OC in the field and its integration into large-scale post-harvest grading systems.
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Affiliation(s)
- Tianqi Gao
- Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | - Yoshito Saito
- Institute of Science and Technology, Niigata University, Niigata, Japan.
| | | | - Makoto Kuramoto
- Advanced Research Support Center, Ehime University, Ehime, Japan
| | - Keiji Konagaya
- Faculty of Collaborative Regional Innovation, Ehime University, Ehime, Japan
| | | | | | | | - Naoshi Kondo
- Graduate School of Agriculture, Kyoto University, Kyoto, Japan
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Marín-Méndez JJ, Luri Esplandiú P, Alonso-Santamaría M, Remirez-Moreno B, Urtasun Del Castillo L, Echavarri Dublán J, Almiron-Roig E, Sáiz-Abajo MJ. Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food. Curr Res Food Sci 2024; 9:100799. [PMID: 39040225 PMCID: PMC11261282 DOI: 10.1016/j.crfs.2024.100799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 06/23/2024] [Indexed: 07/24/2024] Open
Abstract
Knowledge of the energy and macronutrient content of complex foods is essential for the food industry and to implement population-based dietary guidelines. However, conventional methodologies are time-consuming, require the use of chemical products and the sample cannot be recovered. We hypothesize that the nutritional value of heterogeneous food products can be readily measured instead by using hyperspectral imaging systems (NIR and VIS-NIR) combined with mathematical models previously fitted with spectral profiles.118 samples from different food products were collected for building the predictive models using their hyperspectral imaging data as predictors and their nutritional values as dependent variables. Ten different models were screened (Multivariate Linear regression, Lasso regression, Rigde regression, Elastic Net regression, K-Neighbors regression, Decision trees regression, Partial Least Square, Support Vector Machines, Gradient Boosting regression and Random Forest regression). The best results were obtained with Ridge regression for all parameters. The best performance was for estimating the protein content with a RMSE of 1.02 and a R2 equal to 0.88 in a test set, following by moisture (RMSE of 2.21 and R2 equal to 0.85), energy value (RMSE of 21.84 and R2 equal to 0.76) and total fat (RMSE of 2.17 and R2 equal to 0.72). The performance with carbohydrates (RMSE of 2.12 and R2 equal to 0.61) and ashes (RMSE of 0.25 and R2 equal to 0.38) was worse. This study shows that it is possible to predict the energy and nutrient values of processed complex foods, using hyperspectral imaging systems combined with supervised machine learning methods.
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Affiliation(s)
- Juan-Jesús Marín-Méndez
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Paula Luri Esplandiú
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Miriam Alonso-Santamaría
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Berta Remirez-Moreno
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Leyre Urtasun Del Castillo
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Jaione Echavarri Dublán
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Eva Almiron-Roig
- Centre for Nutrition Research, University of Navarra. Pamplona, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - María-José Sáiz-Abajo
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
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Dung CD, Trueman SJ, Wallace HM, Farrar MB, Gama T, Tahmasbian I, Bai SH. Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:114166-114182. [PMID: 37858016 PMCID: PMC10663281 DOI: 10.1007/s11356-023-30344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023]
Abstract
Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.
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Affiliation(s)
- Cao Dinh Dung
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Potato, Vegetable and Flower Research Center - Institute of Agricultural Science for Southern Vietnam, Thai Phien Village, Ward 12, Da Lat, Lam Dong, Vietnam
| | - Stephen J Trueman
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Helen M Wallace
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Michael B Farrar
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Tsvakai Gama
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
| | - Iman Tahmasbian
- Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD, 4350, Australia
| | - Shahla Hosseini Bai
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia.
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Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I, Cho BK. Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1240361. [PMID: 37662162 PMCID: PMC10471194 DOI: 10.3389/fpls.2023.1240361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
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Affiliation(s)
- Umuhoza Aline
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | - Tanima Bhattacharya
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
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Haghbin N, Bakhshipour A, Zareiforoush H, Mousanejad S. Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics. PLANT METHODS 2023; 19:53. [PMID: 37268945 PMCID: PMC10236597 DOI: 10.1186/s13007-023-01032-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1st derivative, and Savitzky-Golay 2nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits' firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky-Golay 1st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R2) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R2 values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.
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Affiliation(s)
- Najmeh Haghbin
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Hemad Zareiforoush
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Sedigheh Mousanejad
- Department of Plant Protection, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
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De Silva AL, Trueman SJ, Kämper W, Wallace HM, Nichols J, Hosseini Bai S. Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition. PLANTS (BASEL, SWITZERLAND) 2023; 12:558. [PMID: 36771641 PMCID: PMC9921287 DOI: 10.3390/plants12030558] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/16/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400-1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R2P) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R2P = 0.55, RPD = 1.52; P: R2P = 0.77, RPD = 2.11; K: R2P = 0.77, RPD = 2.12; Ca: R2P = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion.
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Affiliation(s)
| | | | | | | | | | - Shahla Hosseini Bai
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia
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Fang J, Jin X, Wu L, Zhang Y, Jia B, Ye Z, Heng W, Liu L. Prediction Models for the Content of Calcium, Boron and Potassium in the Fruit of 'Huangguan' Pears Established by Using Near-Infrared Spectroscopy. Foods 2022; 11:foods11223642. [PMID: 36429233 PMCID: PMC9689733 DOI: 10.3390/foods11223642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/21/2022] [Accepted: 11/07/2022] [Indexed: 11/16/2022] Open
Abstract
It has been proved that the imbalance of the proportion of elements of 'Huangguan' pears in the pulp and peel, especially calcium, boron and potassium, may be important factors that can seriously affect the pears' appearance quality and economic benefits. The objective of this study was to predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears nondestructively and conveniently by using near-infrared spectroscopy (900-1700 nm) technology. Firstly, 12 algorithms were used to preprocess the original spectral data. Then, based on the original and preprocessed spectral data, full-band prediction models were established by using Partial Least Squares Regression and Gradient Boosting Regression Tree. Finally, the characteristic wavelengths were extracted by Genetic Algorithms to establish the characteristic wavelength prediction models. According to the prediction results, the value of the determination coefficient of the prediction sets of the best prediction models for the three elements all reached ideal levels, and the values of their Relative analysis error also showed high levels. Therefore, the micro near-infrared spectrometer based on machine learning can predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears accurately and quickly. The results also provide an important scientific theoretical basis for further research on the degradation of the quality of 'Huangguan' pears caused by a lack of nutrients.
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Affiliation(s)
- Jing Fang
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Xiu Jin
- School of Information and Computer Science, Anhui Agriculture University, 130 Changjiang West Road, Hefei 230036, China
| | - Lin Wu
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Yuxin Zhang
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Bing Jia
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Zhenfeng Ye
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Wei Heng
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
| | - Li Liu
- School of Horticulture, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China
- Correspondence: ; Tel.: +86-18096616663
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Samrat NH, Johnson JB, White S, Naiker M, Brown P. A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger. Foods 2022; 11:foods11050649. [PMID: 35267285 PMCID: PMC8909893 DOI: 10.3390/foods11050649] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 12/10/2022] Open
Abstract
Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.
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Affiliation(s)
- Nahidul Hoque Samrat
- School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, QLD 4670, Australia; (S.W.); (P.B.)
- Institute for Future Farming Systems, Central Queensland University, Bundaberg, QLD 4670, Australia
- Correspondence:
| | - Joel B. Johnson
- School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia; (J.B.J.); (M.N.)
- Institute for Future Farming Systems, Central Queensland University, Rockhampton, QLD 4701, Australia
| | - Simon White
- School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, QLD 4670, Australia; (S.W.); (P.B.)
- Institute for Future Farming Systems, Central Queensland University, Bundaberg, QLD 4670, Australia
| | - Mani Naiker
- School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia; (J.B.J.); (M.N.)
- Institute for Future Farming Systems, Central Queensland University, Rockhampton, QLD 4701, Australia
| | - Philip Brown
- School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, QLD 4670, Australia; (S.W.); (P.B.)
- Institute for Future Farming Systems, Central Queensland University, Bundaberg, QLD 4670, Australia
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Artavia G, Cortés-Herrera C, Granados-Chinchilla F. Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis. Foods 2021; 10:1081. [PMID: 34068197 PMCID: PMC8152966 DOI: 10.3390/foods10051081] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/13/2021] [Accepted: 03/19/2021] [Indexed: 12/28/2022] Open
Abstract
This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.
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Affiliation(s)
- Graciela Artavia
- Centro Nacional de Ciencia y Tecnología de Alimentos, Sede Rodrigo Facio, Universidad de Costa Rica, San José 11501-2060, Costa Rica;
| | - Carolina Cortés-Herrera
- Centro Nacional de Ciencia y Tecnología de Alimentos, Sede Rodrigo Facio, Universidad de Costa Rica, San José 11501-2060, Costa Rica;
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Tahmasbian I, Wallace HM, Gama T, Hosseini Bai S. An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.110893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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